Introduction

This code collates data via the NY Times COVID-19 tracking project by relevant counties. Results are updated every day at 4:00 PM EST.

Use the table of contents on the left to navigate to your state. Then, click the appropriate tab to select your county.

Risk assessments for each county are set according to Harvard Global Health Institute parameters. For reference and comparison, you can view their tool at https://globalepidemics.org/key-metrics-for-covid-suppression/.

library(readr)
library(tidyverse)
library(usmap)
library(viridis)
library(scales)
library(sf)
library(plotly)
library(patchwork)
library(kableExtra)

#Get past and today's COVID data
todayDataURL = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/live/us-counties.csv'
today_covid_data = read_csv(url(todayDataURL))
pastDataURL = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'
past_covid_data = read_csv(url(pastDataURL))

cum_covid_data = rbind(past_covid_data, select(today_covid_data, c("date", "county", "state", "fips", "cases", "deaths")))
cum_covid_data = cum_covid_data[with(cum_covid_data, order(date, state, county)),]
# cum_covid_data = cum_covid_data %>%
#   filter(date > as.Date("2020-04-30"))

# NYT data lumps NYC boroughs together, so need to manually create those counties to plot.  For simplicity's sake, I'm just assigning the total NY count to all boroughs
NYCdata = cum_covid_data %>%
  filter(county == "New York City")

Manhattan = NYCdata
Manhattan$fips = 36061
Manhattan$county = "Manhattan"

Bronx = NYCdata
Bronx$fips = 36005
Bronx$county = "Bronx"

Queens = NYCdata
Queens$fips = 36081
Queens$county = "Queens"

Brooklyn = NYCdata
Brooklyn$fips = 36047
Brooklyn$county = "Brooklyn"

Staten_Island = NYCdata
Staten_Island$fips = 36085
Staten_Island$county = "Staten Island"

cum_covid_data = rbind(cum_covid_data, Manhattan, Bronx, Queens, Brooklyn, Staten_Island)

cum_covid_data = cum_covid_data[with(cum_covid_data, order(date, state, county)),]

#Get dates
today_date = cum_covid_data$date[nrow(cum_covid_data)]-1 #CHECK THIS!! yes, it appears that current data lags behind a day
today_date_MDY = format(today_date, format="%B %d %Y")

yesterday_date = cum_covid_data$date[nrow(cum_covid_data)]-7#-2 #CHECK THIS!!
yesterday_date_MDY = format(yesterday_date, format="%B %d %Y")

#Compute today's new cases
yesterday_covid_data = filter(cum_covid_data, date == today_date-7)#-2)
today_covid_data2 = filter(cum_covid_data, date == today_date-1) #today's data with modified NYC data
#today (6/22/2020), the NYC data for today and yesterday are the same....sooo check that.  As of right now I'm using -2 and -1 days 

#Merge Dataframes
new_case_data = merge(yesterday_covid_data, today_covid_data2, by = "fips")
data(countypop) # Using 2015 census estimates provided by usmap package.  It's just easier.
new_case_data = merge(new_case_data, countypop, by = "fips")
new_case_data = new_case_data %>%
  mutate(new_cases = ((cases.y - cases.x)/7)/pop_2015*100000) #Taking 7 day average here)

Full US Map

# Create USA MAP: TOTAL CASES
states <- plot_usmap("states", color = "black", fill = alpha(0.01), size = 1)

counties_total <- plot_usmap(data = today_covid_data2, values = "cases", color = "black", size = 0.5)

USA_map_total = ggplot() +
  counties_total$layers[[1]] + #counties needs to be on top of states for this to work
  states$layers[[1]] +
  counties_total$theme + 
  coord_equal()+
  scale_fill_viridis(limits = c(0, 3000), oob = squish, breaks = c(0, 1000, 2000, 3000), labels = c("0", "1000", "2000", ">3000"))+
  labs(fill = "Total cases")+
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5, size = 14))+
  ggtitle(paste('Total Cases:', today_date_MDY))


# Create USA MAP: NEW CASES
counties_new <- plot_usmap(data = new_case_data, values = "new_cases", color = "black", size = 0.5)

USA_map_new = ggplot() +
  counties_new$layers[[1]] + #counties needs to be on top of states for this to work
  states$layers[[1]] +
  counties_new$theme + 
  coord_equal()+
  scale_fill_viridis(limits = c(0, 25), oob = squish, breaks = c(0, 5, 10, 15, 20, 25), labels = c("0", "5", "10", "15", "20",  ">25"))+
  labs(fill = "New cases (per 100k)")+
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5, size = 14))+
  ggtitle(paste('New Cases per 100,000 residents: ', yesterday_date_MDY, "-", today_date_MDY, sep = ""))


USA_map_total

USA_map_new

Massachusetts

MA_map = plot.state("MA")
MA_map

Essex County

MA_Essex = info.county("Massachusetts", "Essex", 25009)
risk_level = MA_Essex[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MA_Essex[[7]]

ESSEX COUNTY SUMMARY:

Essex county reported 50 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 44.7, or 5.8 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

MA_Essex[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MA_Essex[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Suffolk County

MA_Suffolk = info.county("Massachusetts", "Suffolk", 25025)
risk_level = MA_Suffolk[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MA_Suffolk[[7]]

SUFFOLK COUNTY SUMMARY:

Suffolk county reported 83 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 54.4, or 7 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

MA_Suffolk[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MA_Suffolk[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Middlesex County

MA_Middlesex = info.county("Massachusetts", "Middlesex", 25017)
risk_level = MA_Middlesex[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MA_Middlesex[[7]]

MIDDLESEX COUNTY SUMMARY:

Middlesex county reported 82 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 70.6, or 4.5 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

MA_Middlesex[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MA_Middlesex[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Barnstable County

MA_Barnstable = info.county("Massachusetts", "Barnstable", 25001)
risk_level = MA_Barnstable[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MA_Barnstable[[7]]

BARNSTABLE COUNTY SUMMARY:

Barnstable county reported 11 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 10.6, or 4.9 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

MA_Barnstable[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MA_Barnstable[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

New Hampshire

NH_map = plot.state("NH")
NH_map

Rockingham County

NH_Rockingham = info.county("New Hampshire", "Rockingham", 33015)
risk_level = NH_Rockingham[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = NH_Rockingham[[7]]

ROCKINGHAM COUNTY SUMMARY:

Rockingham county reported 4 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 6.3, or 2.1 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

NH_Rockingham[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

NH_Rockingham[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Hillsborough County

NH_Hillsborough = info.county("New Hampshire", "Hillsborough", 33011)
risk_level = NH_Hillsborough[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = NH_Hillsborough[[7]]

HILLSBOROUGH COUNTY SUMMARY:

Hillsborough county reported 16 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 18.1, or 4.5 cases per 100,000 residents.
Currently, the level of risk is MODERATE.

NH_Hillsborough[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

NH_Hillsborough[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Virginia

VA_map = plot.state("VA")
VA_map

Richmond County

VA_Richmond = info.county("Virginia", "Richmond", 51159)
risk_level = VA_Richmond[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = VA_Richmond[[7]]

RICHMOND COUNTY SUMMARY:

Richmond county reported 2 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is -0.1, or -1.6 cases per 100,000 residents.
Currently, the level of risk is LOW.

VA_Richmond[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

VA_Richmond[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Maryland

MD_map = plot.state("MD")
MD_map

Worcester County

MD_Worcester = info.county("Maryland", "Worcester", 24047)
risk_level = MD_Worcester[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MD_Worcester[[7]]

WORCESTER COUNTY SUMMARY:

Worcester county reported 10 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 11.3, or 21.9 cases per 100,000 residents.
Currently, the level of risk is HIGH.

MD_Worcester[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MD_Worcester[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

Anne Arundel County

MD_Anne_Arundel = info.county("Maryland", "Anne Arundel", 24003)
risk_level = MD_Anne_Arundel[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = MD_Anne_Arundel[[7]]

ANNE ARUNDEL COUNTY SUMMARY:

Anne_Arundel county reported 85 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 77.1, or 13.7 cases per 100,000 residents.
Currently, the level of risk is HIGH.

MD_Anne_Arundel[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

MD_Anne_Arundel[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

North Carolina

NC_map = plot.state("NC")
NC_map

Guilford County

NC_Guilford = info.county("North Carolina", "Guilford", 37081)
risk_level = NC_Guilford[[6]] # for some reason text_spec() doesn't like list objects, so need to assign these to their own variables.
risk_color = NC_Guilford[[7]]

GUILFORD COUNTY SUMMARY:

Guilford county reported 110 new case(s) on July 30 2020.
Over the past seven days, the average number of new cases per day is 79.4, or 15.3 cases per 100,000 residents.
Currently, the level of risk is HIGH.

NC_Guilford[[4]]

New cases since beginning of pandemic. Bars represent new cases each day. Line represents the 7-day moving average

NC_Guilford[[5]]

New cases in past 30 days. Bars represent new cases each day. Line represents the 7-day moving average

A work by Phillip C. Desrochers

philcd89@gmail.com

COVID-19 Local Tracking

Phillip Desrochers

31 July, 2020

Introduction

Full US Map

Massachusetts

Essex County

Suffolk County

Middlesex County

Barnstable County

New Hampshire

Rockingham County

Hillsborough County

Virginia

Richmond County

Maryland

Worcester County

Anne Arundel County

North Carolina

Guilford County